PROPOSAL SUMMARY Over 200,000 patients experience in-hospital cardiac arrests (IHCAs) annually in the US; only 17% survive to discharge. Medical emergency teams (METs) are teams of clinicians who work to improve adverse patient outcomes, such as IHCA and hospital mortality, by providing early detection and rapid interventions for clinically deteriorating patients. METs may decrease IHCA by up to 38%, but their impact on other patient outcomes remains inconclusive. MET triggers ? signs or symptoms of clinical deterioration that result in MET activation ? are related to the symptom experience of clinical deterioration and may provide additional insight. Multiple MET triggers commonly co-occur in adult and pediatric MET events, and the presence of multiple MET triggers has been associated with high incidences of IHCA and hospital mortality. However, the patterns in which MET triggers co-occur have not been explored. The concept of MET trigger clusters (similar to symptom clusters) as groups of distinct yet related MET triggers co-occurring in clinical deterioration processes is a novel approach to studying these patterns. Patient and hospital characteristics could provide context to the clinical deterioration symptom experience and MET trigger clusters, and have been associated with adverse patient outcomes. Adapting the symptom management model, this study will examine the clinical deterioration symptom experience by conceptualizing MET trigger clusters and characterizing their relationships to patient characteristics, hospital characteristics, and patient outcomes following MET events. The proposed cross-sectional study will use data from the Get with the Guidelines-Resuscitation registry MET module, which collects data on all MET events at participating facilities throughout the US. This study will separately examine samples of adult (n=34,652) and pediatric (n=1,075) inpatients who have experienced a MET event. Specific aims of the study are to: 1) Identify MET trigger clusters among inpatients using cluster analysis methods; 2) Examine how contextual factors (i.e., patient and hospital characteristics) are associated with MET trigger clusters; and 3) Determine the association between MET trigger clusters and patient outcomes after controlling for contextual factors (i.e., patient and hospital characteristics). Statistical methods, including cluster analyses and multilevel models, will be used to develop MET trigger clusters and determine their associations with patient characteristics, hospital characteristics, and patient outcomes. This study and accompanying training plan will provide foundational experience and skills in health systems and health services research for a developing nurse scientist focusing on MET and resuscitation practices. The concept of MET trigger clusters is innovative, and may help develop clinical decision support tools or inform machine learning algorithms to improve early recognition of clinical deterioration and patient outcomes. This study aligns with recent recommendations published by the National Institute of Nursing Research for advancing symptom science through symptoms clusters, and will help in developing a scientific basis for clinical nursing practice.